Data Management Protocol - Version 3.0 – 02/2018
BLM’s Terrestrial Assessment, Inventory, and Monitoring (AIM)
2018 Field Season
Data Management Protocol
VERSION 3.0
Produced by BLM National Operations Center and ARS-USDA Jornada Experimental Range – 02/2018
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Contents
1.0 Why Manage Data?
2.0 Project Lead Data Management
3.0 Field Crew Data Management
3.1 Field Crew Quality Assurance (QA): During data collection
3.2 FIELD CREW Quality Control: After data collection
4.0 State AIM Monitoring Coordinator QA and QC
4.1 State AIM Monitoring Coordinator QA: during data collection
4.2 State AIM Monitoring Coordinator QC: after data collection
4.3 Data transfer
5.0 National Operations Center QA and QC
5.1 National Operations Center QA: during data collection
5.2 National Operations Center QC: after data collection
References
Appendix I. Field Office End-of-Season QC Checklist
Appendix II. State Office End-of-Season QC Checklist
Appendix III. Terrestrial AIM Ingestion Decision Tree (updated)
Appendix IV. End of Field Season Implementation Summary
Appendix V. Missing Data/Known Error Tracking Template
Appendix VI. Plot Tracking Excel Documentation
Appendix VII. Plot Tracking Rejection Criteria
1.0Why Manage Data?
Data are the infrastructure of science. Sound data are critical to form the foundation for good scientific decisions and adaptive management.
The purpose of this protocol is to define the Data Management, Quality Assurance (QA), and Quality Control (QC) step for all terrestrial Assessment, Inventory, and Monitoring (AIM) efforts at different levels of management - Project Leads, Field Crews, State Monitoring Coordinators, and the National Operations Center (NOC). This goal of this protocol is to be a guide for how to achieve low levels of error and to establish clear responsibilities for each party involved in the monitoring process.
Project Leads can refer to anyone below the State level who is playing a more active management role in data collection. Oftentimes this is someone at the field office or district level. In some cases, multiple people can participate in the Project Lead roles and responsibilities. There should be BLM staff engaged in this role and this person should have final OK on the data before it is passed to the state level.
Field Crews are the folks actually collecting the data – this can be a crew hired specifically for data collection or field office folks.
State Monitoring Coordinators may be specifically dedicated to AIM or tasked with the responsibilities, but should be someone who is engaged with AIM data collection throughout the entire season, for their entire state.
The National Operations Center refers to the Data Management Team at the NOC who are dedicated to QA&QC at the national level as well as aggregating and serving the data back out. The protocol focuses on field data collected using the Database for Inventory, Monitoring, and Assessment (DIMA) (USDA-ARS Jornada Experimental Range, 2017), which is used exclusively by the Terrestrial AIM program for electronic data capture.
Datamanagement is the process, and means of organizing, and storing data so that these data can be used appropriately to create information and guide sound management decisions. Data management is a key tenet of AIM and is critical to the success of the AIM program. AIM data includes, but are not limited to, management and monitoring objectives (Monitoring Design Worksheet), sample design decisions (Plot Tracking), geospatial input layers, field quantitative data (DIMA), calibration data, field notes, and quality assurance and quality control notes (Missing Data Template), and the final interpretation and assessment of these data (Figure 1 and Table 1).
Quality assurance (QA) and quality control (QC) are the processes of ensuring data integrity and minimizing measurement errors throughout the entire monitoring process (Herrick et. al., 2015). These processes, combined with sound data organization work together to produce a robust data management plan. This document focuses on some of the key data organization and QA and QC steps required for all BLM AIM data.
Although practicing QA and QC may seem tedious, it is a critical step in the monitoring process. Careful attention must be paid to the QA and QC processes because errors in the data will be amplified as the data are used to make land management decisions. QA and QC should be implemented throughout the data life cycle, but in this document we focus on measures that can be taken during and just after data collection.
The spirit of these requirements is to protect us all from a bad investment in monitoring! Rigorous QA and QC strategies make our dataset stronger and more defensible.
Figure 1 defines the Data Management steps throughout the monitoring process and will shift depending on when your field season begins.
NOC Required Data / Data Management Tool- AIM Field Data
- AIM Calibration Data
- Plot Status
- Missing data (errors in dataset)
- Photos
- Description of the project, season, issues/concerns, successes, etc.
2.0 Project Lead Data Management
2.1 Before Monitoring Begins
Every monitoring effort needs to have background information regarding the monitoring effort, including monitoring and management objectives, how the sample design was done, the stratification that was used, supplemental methods required to meet monitoring objectives, etc. This information should be documented in the Monitoring Design Worksheet (see aim.landscapetoolbox.org/design) and should be completed with assistance from the National Operations Center (please contact Emily Kachergis, for terrestrial efforts). The Monitoring Design worksheet provides a step-by-step template for designing BLM AIM efforts. For additional information on the concepts described in the worksheet, see the AIM Landscape Toolbox site Design Page. This worksheet will need to be filled out prior to field data collection.
2.2 Project Lead Quality Assurance (QA): during data collection
Although the bulk of the QA is done by the field crew, there are specific QA guidelines for the project lead at the field office or district office:
a)Understand the AIM data management process and work to maintain this process year round. Where needed, build local data management practices to handle supplemental data and address additional data needs.
b)Ensure all field crew members understand the AIM methods, have used online training resources, and at the very least that the field crew lead has attended an AIM field methods training in the last 3 years. It is preferred that your whole crew attends training, but with travel and budget limitations, the bare minimum is that the crew lead has attended a recent training (See Appendix III. Terrestrial AIM Ingestion Decision Tree for additional ingestion requirements).
c)Participate in the Office Plot Rejection process – either assist your crew lead in evaluating the plots in the office prior to field sampling or perform this task yourself. For more information see the Sample Point Evaluation page on the website: Document these evaluations in the Plot Tracking Excel file (Appendix VI.)
d)Keep communication open with the field crew lead to answer any questions that may arise during the field season. Plant identification and soils in particular may require extra assistance. Direct the crew towards experts if you cannot answer the questions directly.
e)Check up on the field crew (ideally in the field) periodically to make sure they are properly implementing the AIM methods throughout the field season.
f)Be a resource or identify someone in your office who can be a resource for plant identification and develop guidelines on what information you want maintained for unknown plants.
g)Ensure that regular calibrations (monthly, when ecotype changes, or when new crew member joins) are completed by checking your field crew’s DIMA periodically.
h)Back up your data.
2.3 PROJECT Lead Quality Control (QC): after Data collection
The project lead gets all data from their field crew lead at the end of the field season and conducts the following QC:
a)Check field crew calibration results, once after their first hitch and periodically throughout the field season.
b)Quality control (QC) check the field crew’s DIMA, once after their first hitch and periodically throughout the season to ensure data quality. You can use the Field Office End of Season checklist (Appendix I.) as reference for what to look for as well as making sure each method is complete. Make sure to be very thorough at the beginning of the season so the field crew doesn’t consistently make the same mistake. Seek to catch mistakes early.
c)QC the field crew’s final DIMA: see Appendix I.
d)Maintain close communication with the field crew lead during the QC process – confirm they are following the steps in the field crew section of this protocol.
e)Store calibration data in a DIMA that is separate from the project DIMA and send this to your state AIM monitoring coordinator along with your project DIMA and the rest of the data elements outlined in Table 1.
f)Update the Monitoring Design Worksheet with any relevant information about the season – changes to the design, challenges addressed, future modifications, etc.
g)Make sure that Plot Tracking excel (Appendix VI.) is complete for each plot that was in the sample design (each plot should have a status – Sampled, Rejected or Not Sampled) and that any intensification or targeted plots have been added to the Plot Tracking Excel and the appropriate additional information filled out.
h)See that any missing data or known errors in the dataset have been documented by your field crew in the Missing Data/Known Errors excel (see Appendix V.).
i)With the assistance of your field crew lead, complete the End of Field Season Implementation Summary (see Appendix IV. End of Field Season Implementation Summary).
Transfer all data/documents within 1 month of the conclusion of data collection or as soon as possible to your state AIM monitoring coordinator (see contact list at aim.landscapetoolbox.org/learn-3/contact/). When sending your final data and documents please confirm all required data/documents are sent. It is best to confirm all data/documents are completed in full with the field crew lead as they are considered the “plot experts” and will be able to assist in developing the documents required.
3.0 Field Crew Data Management
3.1 Field Crew Quality Assurance (QA): During data collection
Do not underestimate the importance of QA steps. Any decisions made in the field will have direct effects on how the data are portrayed at the many levels of use in the future; meaning one data error could be magnified as data are analyzed and interpreted. Follow the basic steps below to help ensure data integrity (Herrick et. al., 2015):
3.1.1 Field Crew General QA
Continuous QA Steps:
a)Practice proper technique for all field methods (follow the guidelines in the Monitoring Manual (Herrick et. al., 2015)).
b)Maintain data organization (e.g., keep paper data sheets organized, use the exact, complete plot names given to you in the sample design, keep DIMA sites organized, etc.). Be consistent.
c)If collecting data on paper, input data into DIMA as soon as possible and have a second field crew member check the work (on your data sheets and in DIMA document which crew member entered the data and which crew member checked the data).
d)Document errors: record any missing data or known errors in the Missing/Known Error Tracking Template (Appendix V.).
e)Solicit expert advice if needed – in particular for plant identification and soils.
f)Back up your data.
Daily QA steps:
a)Review data forms for completeness and correctness. It is preferable to do this while still at the plot and before transect tapes are rolled up in case information is missing and data will have to be collected.
b)Identify unknown plant species. Update those species codes within DIMA.
- Youshould not spend a lot of time at a plot identifying unknown plants.
- If you are unable to quickly identify a plant on a plot, you should follow the Plant Identification Standard Method on page 14 of the 2nd Edition of the Monitoring Manual for Grassland, Shrubland, and Savanna Ecosystems: Volume I (Herrick et. al., 2015).
- Unknown plants should follow the naming convention outlined in the above method.
c)Verify that the end calculations align with your ocular observations (does bare ground % + % foliar cover + between plant cover % = 100 %?).
d)Verify that GPS coordinates make sense. Always take a waypoint at the plot in addition to inputting the GPS coordinates into DIMA as a backup. This waypoint should be entered into your Plot Tracking excel directly from your GPS.
e) Set the defaults on the GPS and stay consistent throughout the field season. Confirm you are using the same datum every day you are out in the field. DO NOT use NAD27.
f)Back up your data after any corrections have been made.
- Frequently make copies of your DIMA and store it in several places.
- You should always have a copy on the BLM network when you have come back from the field as this network is backed up regularly.
- Labeling these versions by date is good way to ensure that you keep track of your various versions.
Weekly QA Steps:
a)Review data for completeness and errors with your Project Lead.
b)Upload and name photos. Please follow the BLM standard photo naming protocol found in Section 3.2.2.
c)Identify any remaining unknown plant species. Update those species in DIMA.
- If you have attempted to key out unknown plant species and are unable to, seek out a botanist for assistance.
- Do not leave all species identification to the end of your season - specimens will not last an entire field season.
d)Update Plot Tracking excel file (Appendix VI.) with the status of each plot sampled or rejected.
- Rejected plots should include rejection reason and any associated comments and sampled plots should have waypoint from GPS, date visited, and any other pertinent information.
- Any plots sampled that were not in your project sample design should be added to your plot tracking excel and labeled as Targeted in the ‘Random or Targeted?’ field. Targeted plots refer to plots that were not randomly placed – one common example is if you sample a key site using AIM methods.
e)Back up your data.
Monthly and Change in Ecosystem Type QA Steps:
a)Each month or in a substantially different plant community and vegetation structure (e.g., mountain sagebrush to cheat grass, grassland to PJ encroachment, etc.) than previously encountered, you should calibrate data gatherers (see section 3.1.2) for each method in the protocol (please see the 2nd Edition of the Monitoring Manual for Grassland, Shrubland, and Savanna Ecosystems: Volume I for detailed instructions).
b)Store this calibration data in a separate DIMA from your project data.
c)Review data for completeness and errors with an ecosystem expert or team leader.
d)Back up your data.
3.1.2 FIELD Crew Calibration
Field crew calibration is a crucial part of the quality assurance process. Calibration ensures accurate data collection and guarantees that data are collected consistently among the field crew members, including the field crew lead. Calibration should occur immediately following training, each time data collection begins in a new ecosystem type, when someone new joins the crew (for a day or for the entire season), or monthly, whichever comes first, throughout the field season. During each calibration, the field crew lead should also observe each field crew member for proper technique and should correct methodological problems immediately.
For clear guidance on how to record and document calibration data, please refer to calibration section in the 2nd Edition of the Monitoring Manual for Grassland, Shrubland, and Savanna Ecosystems: Volume 1 (Herrick et. al., 2015). Calibration data should be maintained and given to the NOC along with all other data at the end of the season in a separate, calibration DIMA. Each DIMA should be clearly labeled.
3.1.3 Field Crew DIMA QA
When you are using DIMA to input data, you will notice there are “checks” in place to assure data accuracy. DIMA provides a consistent approach to the data collection of the standardized methods and will help with the quality assurance process. Only data from DIMA will be uploaded into the national Terrestrial AIM Database (TerrADat) (see Appendix III. Terrestrial AIM Ingestion Decision Tree). Use the following basic steps to help maintain your DIMA. Additional information and tutorials on how to perform some of these tasks in DIMA can be found on the Jornada DIMA website ( and on aim.landscapetoolbox.org.
a)Make sure to upgrade to the BLM-approved version of DIMA. Due to upgrade lags within the BLM, the “BLM-approved” DIMA may NOT necessarily be the most current version available from the USDA-ARS Jornada Experimental Range website ( Please check the AIM Landscape Toolbox website (aim.landscapetoolbox.org/data-collection/field-season-preparation-2/) for the DIMA version currently used by the BLM or contact the NOC.
b)Be consistent with your naming schema for your sites. Many field crews use the strata from their sample design (BpS, ecological sites, etc.) to separate sites.